Cauda EVs tRNAs analysis using data from both runs

Libraries required for data analysis

source("src.R")

Data: read counts table from both runs

tRNAs_3 <- read.table(
  file = "./run3_excerpt_counts/exceRpt_tRNA_ReadCounts.txt",
  header = TRUE, sep = "", row.names = 1
)
tRNAs_3 <- as.data.frame(tRNAs_3)
counts_total_run3 <- tRNAs_3
tRNAs_4 <- read.table(
  file = "run4_excerpt_counts_/exceRpt_tRNA_ReadCounts.txt",
  header = TRUE, sep = "", row.names = 1
)

tRNAs_4 <- as.data.frame(tRNAs_4)

counts_total_run3 <- tRNAs_3
counts_total_run4 <- tRNAs_4

colnames(counts_total_run3) <- c(
  "MS28F1_sample11", "MS28F1_sample14",
  "MS28F1_sample2", "MS28F1_sample3",
  "MS28F1_sample4", "MS28F1_sample5",
  "MS28F1_sample6", "MS28F1_sample7",
  "MS28F1_sample8", "MS28F1_sample9"
)

colnames(counts_total_run4) <- c(
  "MS28F1_sample11_2", "MS28F1_sample14_2",
  "MS28F1_sample2_2", "MS28F1_sample3_2",
  "MS28F1_sample4_2", "MS28F1_sample5_2",
  "MS28F1_sample6_2", "MS28F1_sample7_2",
  "MS28F1_sample8_2", "MS28F1_sample9_2"
)


mlist <- list(counts_total_run3, counts_total_run4)
df <- mlist[[1]]
for (i in 2:length(mlist)) {
  df <- merge(df, mlist[[i]], by = "row.names", all = T)
  rownames(df) <- df$Row.names
  df <- df[, !(names(df) %in% "Row.names")]
  knitr::kable(head(df))
}

Specifying the group & batch while filterByExprs()

counts_total <- df
d <- data.frame(
  row.names = colnames(counts_total),
  group = c(
    "G2", "G1", "G2", "G1", "G2", "G1", "G2", "G1", "G1", "G2",
    "G1", "G2", "G2", "G1", "G2", "G1", "G2", "G1", "G2", "G1"
  ),
  batch = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)
)
counts_total[is.na(counts_total)] <- 0
dds <- calcNormFactors(DGEList(counts_total), method = "TMM")
dds$samples$group <- c(
  "G2", "G1", "G2", "G1", "G2", "G1", "G2", "G1", "G1",
  "G2", "G1", "G2", "G2", "G1", "G2", "G1", "G2", "G1", "G2", "G1"
)
dds$samples$batch <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)
mm <- model.matrix(~ batch + group, data = d)

dds <- dds[which(filterByExpr(dds, design = mm, min.count = 10, min.prop = 0.8)), ]
en <- log1p(edgeR::cpm(dds)) # normalization by counts per million

Differential Expression analysis without Surrogate Variable analysis

dds <- estimateDisp(dds, mm)
fit <- glmLRT(glmFit(dds, mm), coef = "groupG2")
res_without_ruvseq <- as.data.frame(topTags(fit, Inf))
knitr::kable(head(res_without_ruvseq, n = 20))

	logFC	logCPM	LR	PValue	FDR
Tyr	-0.4893745	7.935688	3.8666022	0.0492561	0.5421865
Ile	-0.4297720	9.694536	3.0391174	0.0812801	0.5421865
Cys	0.2972731	12.607438	2.6750203	0.1019340	0.5421865
Asp	-0.2428079	12.437206	2.0769683	0.1495369	0.5421865
Glu	0.2244529	17.474000	1.9452016	0.1631049	0.5421865
SeC	0.2683224	10.280610	1.8293835	0.1762004	0.5421865
Pro	0.2254161	9.635737	1.7309576	0.1882886	0.5421865
Val	0.3290303	14.720771	1.6632971	0.1971587	0.5421865
Ser	0.2423846	10.004283	1.2642794	0.2608426	0.6376153
Gly	0.1848298	19.674351	1.0426427	0.3072076	0.6758568
Arg	-0.1978367	10.647809	0.8140622	0.3669220	0.7338440
iMet	0.1298134	11.672785	0.5572120	0.4553858	0.8348739
Gln	0.1184996	10.309014	0.2377104	0.6258647	0.9179376
His	0.1008914	12.581947	0.1950451	0.6587504	0.9179376
Lys	-0.0569287	14.572036	0.1313012	0.7170867	0.9179376
Leu	0.0523660	10.795842	0.0894270	0.7649068	0.9179376
Ala	0.0545848	10.771378	0.0837683	0.7722542	0.9179376
Asn	-0.0582296	9.239539	0.0459426	0.8302802	0.9179376
Thr	-0.0424330	9.346387	0.0205495	0.8860129	0.9179376
Phe	0.0314313	6.912327	0.0183823	0.8921522	0.9179376

PCA plots without Surrogate Variable analysis

All samples (normalized by counts-per-million), colored by treatment group

plgINS::plPCA(en,
  colorBy = dds$samples$group,
  add.labels = FALSE, points.size = 15
)

All samples (normalized by counts-per-million), colored by library size

Note: Even after the normalization, the libraries are separated based on lib.size, without surrogate variable analysis Therefore, Surrogate variable analysis is necessary ###

plgINS::plPCA(en,
  colorBy = dds$samples$lib.size,
  add.labels = FALSE, points.size = 15
)

All samples (normalized by counts-per-million), colored by sequencing batch

Note: Even after the normalization, the libraries are separated based on the sequencing batch, without surrogate variable analysis Therefore, Surrogate variable analysis is necessary

plgINS::plPCA(en,
  colorBy = dds$samples$batch,
  add.labels = FALSE, points.size = 15
)

Differential Expression analysis with Surrogate Variable analysi, SV1

re <- RUVSeq::RUVs(en,
  cIdx = row.names(en), k = 1,
  scIdx = RUVSeq::makeGroups(d$group), isLog = TRUE
)
d$SV1 <- re$W[, 1] # add the variable (suppose I have only one) to the colData dataframe
mm <- model.matrix(~ SV1 + batch + group, data = d)
dds <- estimateDisp(dds, mm)
fit <- glmLRT(glmFit(dds, mm), coef = "groupG2")
res_with_ruvseq1 <- as.data.frame(topTags(fit, Inf))
knitr::kable(head(res_with_ruvseq1, n = 20))

	logFC	logCPM	LR	PValue	FDR
Cys	0.3149355	12.610101	3.6056944	0.0575820	0.3416520
Ile	-0.2592603	9.666343	3.5504357	0.0595298	0.3416520
SeC	0.3189241	10.290391	3.5327978	0.0601662	0.3416520
Tyr	-0.3235718	7.874531	3.4798988	0.0621185	0.3416520
Pro	0.2385796	9.632674	2.5146838	0.1127903	0.4763612
Glu	0.2060507	17.474065	2.0768625	0.1495473	0.4763612
Asp	-0.1918334	12.442074	2.0563839	0.1515695	0.4763612
Ser	0.2383967	9.973714	1.3190278	0.2507666	0.6197481
Ala	0.1386194	10.757208	1.2480900	0.2639176	0.6197481
Val	0.2605593	14.721542	1.1445959	0.2846833	0.6197481
Gly	0.1677909	19.674375	1.0240802	0.3115531	0.6197481
iMet	0.1492808	11.662440	0.9178337	0.3380444	0.6197481
Asn	0.1740746	9.227860	0.7597973	0.3833920	0.6365502
Trp	0.1403309	9.786448	0.6458976	0.4215832	0.6365502
Thr	0.1810413	9.332521	0.6120646	0.4340115	0.6365502
His	0.1343053	12.584365	0.3713364	0.5422767	0.7216184
Leu	0.0895625	10.782906	0.3438519	0.5576142	0.7216184
Gln	0.1270955	10.295692	0.2756150	0.5995898	0.7245608
Arg	-0.0585177	10.635208	0.2378586	0.6257571	0.7245608
Phe	0.0833018	6.792185	0.1871275	0.6653182	0.7318501

PCA plots with Surrogate variable analysis, SV=1

All samples, colored by treatment group

plgINS::plPCA(re$normalizedCounts,
  colorBy = d$group,
  add.labels = FALSE, points.size = 15
)

All samples, colored by library size

Note: We can observe that the samples are clustering more by groups & spread by counts. Libraries with high/low counts are not clustered together anymore. No effect of library size, keeping the group effect:

plgINS::plPCA(re$normalizedCounts,
  colorBy = dds$samples$lib.size,
  add.labels = FALSE, points.size = 15
)

All samples, colored by sequencing batch

Note: We can observe that the samples are corrected for sequencing batch effect:

plgINS::plPCA(re$normalizedCounts,
  colorBy = dds$samples$batch,
  add.labels = FALSE, points.size = 15
)

References

report::cite_packages(sessionInfo())

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SessionInfo

devtools::session_info() %>%
  details::details()


─ Session info ───────────────────────────────────────────────────────────────
 setting  value                       
 version  R version 4.0.4 (2021-02-15)
 os       Ubuntu 16.04.7 LTS          
 system   x86_64, linux-gnu           
 ui       X11                         
 language (EN)                        
 collate  de_DE.UTF-8                 
 ctype    de_DE.UTF-8                 
 tz       Europe/Zurich               
 date     2021-06-28                  

─ Packages ───────────────────────────────────────────────────────────────────
 ! package                * version  date       lib source        
   annotate                 1.68.0   2020-10-27 [1] Bioconductor  
   AnnotationDbi          * 1.52.0   2020-10-27 [1] Bioconductor  
   AnnotationFilter         1.14.0   2020-10-27 [1] Bioconductor  
   AnnotationHub            2.22.1   2021-04-16 [1] Bioconductor  
   aroma.light              3.20.0   2020-10-27 [1] Bioconductor  
   ash                      1.0-15   2015-09-01 [1] CRAN (R 4.0.4)
   askpass                  1.1      2019-01-13 [1] CRAN (R 4.0.4)
   assertthat               0.2.1    2019-03-21 [1] CRAN (R 4.0.4)
   backports                1.2.1    2020-12-09 [1] CRAN (R 4.0.4)
   bayestestR               0.9.0    2021-04-08 [1] CRAN (R 4.0.4)
   beeswarm                 0.3.1    2021-03-07 [1] CRAN (R 4.0.4)
   Biobase                * 2.50.0   2020-10-27 [1] Bioconductor  
   BiocFileCache            1.14.0   2020-10-27 [1] Bioconductor  
   BiocGenerics           * 0.36.1   2021-04-16 [1] Bioconductor  
   BiocManager              1.30.12  2021-03-28 [1] CRAN (R 4.0.4)
   BiocParallel           * 1.24.1   2020-11-06 [1] Bioconductor  
   BiocVersion              3.12.0   2020-04-27 [1] Bioconductor  
   biomaRt                * 2.46.3   2021-02-09 [1] Bioconductor  
   Biostrings             * 2.58.0   2020-10-27 [1] Bioconductor  
   bit                      4.0.4    2020-08-04 [1] CRAN (R 4.0.4)
   bit64                    4.0.5    2020-08-30 [1] CRAN (R 4.0.4)
   bitops                   1.0-7    2021-04-24 [1] CRAN (R 4.0.4)
   blob                     1.2.1    2020-01-20 [1] CRAN (R 4.0.4)
   bookdown                 0.22     2021-04-22 [1] CRAN (R 4.0.4)
   boot                     1.3-27   2021-02-12 [1] CRAN (R 4.0.4)
   broom                    0.7.7    2021-06-13 [1] CRAN (R 4.0.4)
   bslib                    0.2.4    2021-01-25 [1] CRAN (R 4.0.4)
   cachem                   1.0.4    2021-02-13 [1] CRAN (R 4.0.4)
   callr                    3.7.0    2021-04-20 [1] CRAN (R 4.0.4)
   caTools                  1.18.2   2021-03-28 [1] CRAN (R 4.0.4)
   cellranger               1.1.0    2016-07-27 [1] CRAN (R 4.0.4)
   cli                      2.5.0    2021-04-26 [1] CRAN (R 4.0.4)
   clipr                    0.7.1    2020-10-08 [1] CRAN (R 4.0.4)
   coda                     0.19-4   2020-09-30 [1] CRAN (R 4.0.4)
   codetools                0.2-18   2020-11-04 [1] CRAN (R 4.0.4)
   colorRamps               2.3      2012-10-29 [1] CRAN (R 4.0.4)
   colorspace               2.0-0    2020-11-11 [1] CRAN (R 4.0.4)
   conquer                  1.0.2    2020-08-27 [1] CRAN (R 4.0.4)
   cqn                    * 1.36.0   2020-10-27 [1] Bioconductor  
   crayon                   1.4.1    2021-02-08 [1] CRAN (R 4.0.4)
   crosstalk                1.1.1    2021-01-12 [1] CRAN (R 4.0.4)
   curl                     4.3      2019-12-02 [1] CRAN (R 4.0.4)
   data.table               1.14.0   2021-02-21 [1] CRAN (R 4.0.4)
   DBI                      1.1.1    2021-01-15 [1] CRAN (R 4.0.4)
   dbplyr                   2.1.1    2021-04-06 [1] CRAN (R 4.0.4)
   DelayedArray             0.16.3   2021-03-24 [1] Bioconductor  
   desc                     1.3.0    2021-03-05 [1] CRAN (R 4.0.4)
   DESeq2                 * 1.30.1   2021-02-19 [1] Bioconductor  
   details                  0.2.1    2020-01-12 [1] CRAN (R 4.0.4)
   devtools                 2.4.2    2021-06-07 [1] CRAN (R 4.0.4)
   digest                   0.6.27   2020-10-24 [1] CRAN (R 4.0.4)
   doParallel               1.0.16   2020-10-16 [1] CRAN (R 4.0.4)
   dplyr                  * 1.0.5    2021-03-05 [1] CRAN (R 4.0.4)
   EDASeq                 * 2.24.0   2020-10-27 [1] Bioconductor  
   edgeR                  * 3.32.1   2021-01-14 [1] Bioconductor  
   effectsize               0.4.4-1  2021-04-05 [1] CRAN (R 4.0.4)
   ellipsis                 0.3.1    2020-05-15 [1] CRAN (R 4.0.4)
   emmeans                  1.6.0    2021-04-24 [1] CRAN (R 4.0.4)
   EnhancedVolcano        * 1.8.0    2020-10-27 [1] Bioconductor  
   ensembldb                2.14.1   2021-04-19 [1] Bioconductor  
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   GenomeInfoDbData         1.2.4    2021-03-30 [1] Bioconductor  
   GenomicAlignments      * 1.26.0   2020-10-27 [1] Bioconductor  
   GenomicFeatures          1.42.3   2021-04-01 [1] Bioconductor  
   GenomicRanges          * 1.42.0   2020-10-27 [1] Bioconductor  
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   ggplot2                * 3.3.3    2020-12-30 [1] CRAN (R 4.0.4)
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   gtools                   3.8.2    2020-03-31 [1] CRAN (R 4.0.4)
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[1] /home/ubuntu/R/x86_64-pc-linux-gnu-library/4.0
[2] /usr/local/lib/R/site-library
[3] /usr/lib/R/site-library
[4] /usr/lib/R/library

 P ── Loaded and on-disk path mismatch.